Rolling is a metal forming process to produce a strip with particular dimensions and mechanical properties. While its thickness decreases, the strip is elongated but also experiences expansion in width, referred to as lateral flow. The ambition to make steel production more sustainable, combined with increased customer demands regarding product specifications, requires the manufacturing of dimensionally accurate products while reducing waste during production as much as possible. This is achievable only with exacting process control mediated by accurate and efficient mathematical models describing the material deformation during the process, considering key influencing factors such as process temperature, differences between the steel grades, and interaction of rolls and the strip.
This project strives to develop a new hybrid modelling strategy blending physics-based and data-driven approaches. The physics-based semi-analytical prediction will provide a fast solution to the rolling problem using model order reduction, while the data-driven machine learning correction will convert the solution to a high accuracy. The resulting fast, accurate, and comprehensive lateral flow model for rolling process will enable improved process control for various mill configurations.
In this project, you will report your research during bi-weekly meetings of our research group and frequent meetings with the industrial partner. You are encouraged to present your results at international scientific conferences, and publish them in academic journals. Furthermore, as a researcher, you will be encouraged to tutor MSc students who do their final assignment on parts of the current research project. Your doctoral advisors will be Dr. habil Celal Soyarslan and Prof. Dr. Ton van den Boogaard.
Information and application
Please submit your application before February 15th, 2023, using the "Apply now" button, and include the following:
- curriculum vitae
- letter of motivation
- grades of the BSc and MSc courses
- IELTS or TOEFL score (if necessary)
- contact information of 2 references
The intended starting date is between April and July 2023.
For more information, you can contact Dr. habil Celal Soyarslan by phone: +31 5 3489 7499, or by email: firstname.lastname@example.org
First (online) interviews will be held on Tuesday February 28th and Thursday March 2nd, 2023.
A Game-Based assessment will be used in the selection procedure.
About the organisation
The Faculty of Engineering Technology (ET) engages in education and research of Mechanical Engineering, Civil Engineering and Industrial Design Engineering. We enable society and industry to innovate and create value using efficient, solid and sustainable technology. We are part of a ‘people-first' university of technology, taking our place as an internationally leading center for smart production, processes and devices in five domains: Health Technology, Maintenance, Smart Regions, Smart Industry and Sustainable Resources. Our faculty is home to about 2,900 Bachelor's and Master's students, 550 employees and 150 PhD candidates. Our educational and research programmes are closely connected with UT research institutes Mesa+ Institute, TechMed Center and Digital Society Institute.